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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
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| Paper Title: | Enhancing Low-Resource Machine Translation Through Multilingual Generative Models and Synthetic Data Augmentation |
| Authors Name: | Swapna Vanguru , V Vani , Mr Aziz Siddiqui , Dr P Balakrishna , Dr Ch Rathan Kumar, Dr T Sunil Kumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_209570
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| Published Paper Id: | IJRTI2602009 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i2.209570 |
| Abstract: | Generative Artificial Intelligence (GenAI) has achieved remarkable success in machine translation for high-resource languages; however, low-resource languages continue to suffer from poor translation quality due to limited parallel corpora, complex morphology, and cultural contextualization challenges. This research presents an enhanced generative translation framework tailored for low-resource languages by combining multilingual data augmentation, transformer-based architectures, and culturally aware optimization strategies. The proposed system leverages synthetic dataset generation through back-translation and large language model augmentation, followed by fine-tuning of mT5, mBART, and LLaMA models using parameter-efficient techniques such as LoRA and adapter layers. To reduce hallucinations and improve semantic fidelity, constraint-based decoding and reinforcement learning from human feedback are incorporated. Experimental evaluation conducted on multiple low-resource language pairs demonstrates significant performance gains over baseline models, achieving an average improvement of 8.7% in BLEU score, 6.3% in COMET, and 7.9% in BERTScore. Additionally, hallucination rates are reduced by approximately 35%, while human evaluation indicates improved grammatical correctness and cultural appropriateness in over 82% of translated samples. These results validate the effectiveness of the proposed framework in delivering accurate, context-aware translations for low-resource languages. The study contributes a scalable and deployable GenAI-based translation solution that promotes digital inclusion and supports linguistic preservation in multilingual ecosystems. |
| Keywords: | Low-Resource Machine Translation; Generative AI; Multilingual Transformers; Synthetic Data Augmentation; Parameter-Efficient Fine-Tuning; Hallucination Reduction; Culturally Aware Translation; Semantic Evaluation Metrics. |
| Cite Article: | "Enhancing Low-Resource Machine Translation Through Multilingual Generative Models and Synthetic Data Augmentation", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 2, page no.a64-a71, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602009.pdf |
| Downloads: | 000145 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2602009
Registration ID:209570
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i2.209570
Page No: a64-a71 Country: Hyderabad, Telangana, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602009 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602009 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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